1 INTRODUCTION
1.3 T HE H ISTORY OF A RTIFICIAL I NTELLIGENCE
With the background material behind us, we are ready to cover the development of AI itself.
1.3.1 The gestation of artificial intelligence (1943–1955)
The first work that is now generally recognized as AI was done by Warren McCulloch and Walter Pitts (1943). They drew on three sources: knowledge of the basic physiology and function of neurons in the brain; a formal analysis of propositional logic due to Russell and Whitehead; and Turing’s theory of computation. They proposed a model of artificial neurons in which each neuron is characterized as being “on” or “off,” with a switch to “on” occurring in response to stimulation by a sufficient number of neighboring neurons. The state of a neuron was conceived of as “factually equivalent to a proposition which proposed its adequate stimulus.” They showed, for example, that any computable function could be computed by some network of connected neurons, and that all the logical connectives (and, or, not, etc.) could be implemented by simple net structures. McCulloch and Pitts also suggested that suitably defined networks could learn. Donald Hebb (1949) demonstrated a simple updating rule for modifying the connection strengths between neurons. His rule, now calledHebbian learning, remains an influential model to this day.
HEBBIAN LEARNING
Two undergraduate students at Harvard, Marvin Minsky and Dean Edmonds, built the first neural network computer in 1950. The SNARC, as it was called, used 3000 vacuum tubes and a surplus automatic pilot mechanism from a B-24 bomber to simulate a network of 40 neurons. Later, at Princeton, Minsky studied universal computation in neural networks.
His Ph.D. committee was skeptical about whether this kind of work should be considered
Section 1.3. The History of Artificial Intelligence 17 mathematics, but von Neumann reportedly said, “If it isn’t now, it will be someday.” Minsky was later to prove influential theorems showing the limitations of neural network research.
There were a number of early examples of work that can be characterized as AI, but Alan Turing’s vision was perhaps the most influential. He gave lectures on the topic as early as 1947 at the London Mathematical Society and articulated a persuasive agenda in his 1950 article “Computing Machinery and Intelligence.” Therein, he introduced the Turing Test, machine learning, genetic algorithms, and reinforcement learning. He proposed the Child Programmeidea, explaining “Instead of trying to produce a programme to simulate the adult mind, why not rather try to produce one which simulated the child’s?”
1.3.2 The birth of artificial intelligence (1956)
Princeton was home to another influential figure in AI, John McCarthy. After receiving his PhD there in 1951 and working for two years as an instructor, McCarthy moved to Stan- ford and then to Dartmouth College, which was to become the official birthplace of the field.
McCarthy convinced Minsky, Claude Shannon, and Nathaniel Rochester to help him bring together U.S. researchers interested in automata theory, neural nets, and the study of intel- ligence. They organized a two-month workshop at Dartmouth in the summer of 1956. The proposal states:10
We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hamp- shire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely de- scribed that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer.
There were 10 attendees in all, including Trenchard More from Princeton, Arthur Samuel from IBM, and Ray Solomonoff and Oliver Selfridge from MIT.
Two researchers from Carnegie Tech,11 Allen Newell and Herbert Simon, rather stole the show. Although the others had ideas and in some cases programs for particular appli- cations such as checkers, Newell and Simon already had a reasoning program, the Logic Theorist (LT), about which Simon claimed, “We have invented a computer program capable of thinking non-numerically, and thereby solved the venerable mind–body problem.”12 Soon after the workshop, the program was able to prove most of the theorems in Chapter 2 of Rus-
10This was the first official usage of McCarthy’s termartificial intelligence. Perhaps “computational rationality”
would have been more precise and less threatening, but “AI” has stuck. At the 50th anniversary of the Dartmouth conference, McCarthy stated that he resisted the terms “computer” or “computational” in deference to Norbert Weiner, who was promoting analog cybernetic devices rather than digital computers.
11Now Carnegie Mellon University (CMU).
12Newell and Simon also invented a list-processing language, IPL, to write LT. They had no compiler and translated it into machine code by hand. To avoid errors, they worked in parallel, calling out binary numbers to each other as they wrote each instruction to make sure they agreed.
18 Chapter 1. Introduction sell and Whitehead’sPrincipia Mathematica. Russell was reportedly delighted when Simon showed him that the program had come up with a proof for one theorem that was shorter than the one inPrincipia. The editors of theJournal of Symbolic Logicwere less impressed; they rejected a paper coauthored by Newell, Simon, and Logic Theorist.
The Dartmouth workshop did not lead to any new breakthroughs, but it did introduce all the major figures to each other. For the next 20 years, the field would be dominated by these people and their students and colleagues at MIT, CMU, Stanford, and IBM.
Looking at the proposal for the Dartmouth workshop (McCarthy et al., 1955), we can see why it was necessary for AI to become a separate field. Why couldn’t all the work done in AI have taken place under the name of control theory or operations research or decision theory, which, after all, have objectives similar to those of AI? Or why isn’t AI a branch of mathematics? The first answer is that AI from the start embraced the idea of duplicating human faculties such as creativity, self-improvement, and language use. None of the other fields were addressing these issues. The second answer is methodology. AI is the only one of these fields that is clearly a branch of computer science (although operations research does share an emphasis on computer simulations), and AI is the only field to attempt to build machines that will function autonomously in complex, changing environments.
1.3.3 Early enthusiasm, great expectations (1952–1969)
The early years of AI were full of successes—in a limited way. Given the primitive comput- ers and programming tools of the time and the fact that only a few years earlier computers were seen as things that could do arithmetic and no more, it was astonishing whenever a com- puter did anything remotely clever. The intellectual establishment, by and large, preferred to believe that “a machine can never do X.” (See Chapter 26 for a long list ofX’s gathered by Turing.) AI researchers naturally responded by demonstrating oneX after another. John McCarthy referred to this period as the “Look, Ma, no hands!” era.
Newell and Simon’s early success was followed up with the General Problem Solver, or GPS. Unlike Logic Theorist, this program was designed from the start to imitate human problem-solving protocols. Within the limited class of puzzles it could handle, it turned out that the order in which the program considered subgoals and possible actions was similar to that in which humans approached the same problems. Thus, GPS was probably the first pro- gram to embody the “thinking humanly” approach. The success of GPS and subsequent pro- grams as models of cognition led Newell and Simon (1976) to formulate the famousphysical symbol systemhypothesis, which states that “a physical symbol system has the necessary and
PHYSICAL SYMBOL SYSTEM
sufficient means for general intelligent action.” What they meant is that any system (human or machine) exhibiting intelligence must operate by manipulating data structures composed of symbols. We will see later that this hypothesis has been challenged from many directions.
At IBM, Nathaniel Rochester and his colleagues produced some of the first AI pro- grams. Herbert Gelernter (1959) constructed the Geometry Theorem Prover, which was able to prove theorems that many students of mathematics would find quite tricky. Starting in 1952, Arthur Samuel wrote a series of programs for checkers (draughts) that eventually learned to play at a strong amateur level. Along the way, he disproved the idea that comput-
Section 1.3. The History of Artificial Intelligence 19 ers can do only what they are told to: his program quickly learned to play a better game than its creator. The program was demonstrated on television in February 1956, creating a strong impression. Like Turing, Samuel had trouble finding computer time. Working at night, he used machines that were still on the testing floor at IBM’s manufacturing plant. Chapter 5 covers game playing, and Chapter 21 explains the learning techniques used by Samuel.
John McCarthy moved from Dartmouth to MIT and there made three crucial contribu- tions in one historic year: 1958. In MIT AI Lab Memo No. 1, McCarthy defined the high-level languageLisp, which was to become the dominant AI programming language for the next 30
LISP
years. With Lisp, McCarthy had the tool he needed, but access to scarce and expensive com- puting resources was also a serious problem. In response, he and others at MIT invented time sharing. Also in 1958, McCarthy published a paper entitledPrograms with Common Sense, in which he described the Advice Taker, a hypothetical program that can be seen as the first complete AI system. Like the Logic Theorist and Geometry Theorem Prover, McCarthy’s program was designed to use knowledge to search for solutions to problems. But unlike the others, it was to embody general knowledge of the world. For example, he showed how some simple axioms would enable the program to generate a plan to drive to the airport. The program was also designed to accept new axioms in the normal course of operation, thereby allowing it to achieve competence in new areaswithout being reprogrammed. The Advice Taker thus embodied the central principles of knowledge representation and reasoning: that it is useful to have a formal, explicit representation of the world and its workings and to be able to manipulate that representation with deductive processes. It is remarkable how much of the 1958 paper remains relevant today.
1958 also marked the year that Marvin Minsky moved to MIT. His initial collaboration with McCarthy did not last, however. McCarthy stressed representation and reasoning in for- mal logic, whereas Minsky was more interested in getting programs to work and eventually developed an anti-logic outlook. In 1963, McCarthy started the AI lab at Stanford. His plan to use logic to build the ultimate Advice Taker was advanced by J. A. Robinson’s discov- ery in 1965 of the resolution method (a complete theorem-proving algorithm for first-order logic; see Chapter 9). Work at Stanford emphasized general-purpose methods for logical reasoning. Applications of logic included Cordell Green’s question-answering and planning systems (Green, 1969b) and the Shakey robotics project at the Stanford Research Institute (SRI). The latter project, discussed further in Chapter 25, was the first to demonstrate the complete integration of logical reasoning and physical activity.
Minsky supervised a series of students who chose limited problems that appeared to require intelligence to solve. These limited domains became known asmicroworlds. James
MICROWORLD
Slagle’s SAINTprogram (1963) was able to solve closed-form calculus integration problems typical of first-year college courses. Tom Evans’s ANALOGY program (1968) solved geo- metric analogy problems that appear in IQ tests. Daniel Bobrow’s STUDENTprogram (1967) solved algebra story problems, such as the following:
If the number of customers Tom gets is twice the square of 20 percent of the number of advertisements he runs, and the number of advertisements he runs is 45, what is the number of customers Tom gets?
20 Chapter 1. Introduction
Red
Green
Red
Green
Green
Blue Blue
Red
Figure 1.4 A scene from the blocks world. SHRDLU(Winograd, 1972) has just completed the command “Find a block which is taller than the one you are holding and put it in the box.”
The most famous microworld was the blocks world, which consists of a set of solid blocks placed on a tabletop (or more often, a simulation of a tabletop), as shown in Figure 1.4.
A typical task in this world is to rearrange the blocks in a certain way, using a robot hand that can pick up one block at a time. The blocks world was home to the vision project of David Huffman (1971), the vision and constraint-propagation work of David Waltz (1975), the learning theory of Patrick Winston (1970), the natural-language-understanding program of Terry Winograd (1972), and the planner of Scott Fahlman (1974).
Early work building on the neural networks of McCulloch and Pitts also flourished.
The work of Winograd and Cowan (1963) showed how a large number of elements could collectively represent an individual concept, with a corresponding increase in robustness and parallelism. Hebb’s learning methods were enhanced by Bernie Widrow (Widrow and Hoff, 1960; Widrow, 1962), who called his networks adalines, and by Frank Rosenblatt (1962) with hisperceptrons. Theperceptron convergence theorem(Blocket al., 1962) says that the learning algorithm can adjust the connection strengths of a perceptron to match any input data, provided such a match exists. These topics are covered in Chapter 20.
1.3.4 A dose of reality (1966–1973)
From the beginning, AI researchers were not shy about making predictions of their coming successes. The following statement by Herbert Simon in 1957 is often quoted:
It is not my aim to surprise or shock you—but the simplest way I can summarize is to say that there are now in the world machines that think, that learn and that create. Moreover,
Section 1.3. The History of Artificial Intelligence 21 their ability to do these things is going to increase rapidly until—in a visible future—the range of problems they can handle will be coextensive with the range to which the human mind has been applied.
Terms such as “visible future” can be interpreted in various ways, but Simon also made more concrete predictions: that within 10 years a computer would be chess champion, and a significant mathematical theorem would be proved by machine. These predictions came true (or approximately true) within 40 years rather than 10. Simon’s overconfidence was due to the promising performance of early AI systems on simple examples. In almost all cases, however, these early systems turned out to fail miserably when tried out on wider selections of problems and on more difficult problems.
The first kind of difficulty arose because most early programs knew nothing of their subject matter; they succeeded by means of simple syntactic manipulations. A typical story occurred in early machine translation efforts, which were generously funded by the U.S. Na- tional Research Council in an attempt to speed up the translation of Russian scientific papers in the wake of the Sputnik launch in 1957. It was thought initially that simple syntactic trans- formations based on the grammars of Russian and English, and word replacement from an electronic dictionary, would suffice to preserve the exact meanings of sentences. The fact is that accurate translation requires background knowledge in order to resolve ambiguity and establish the content of the sentence. The famous retranslation of “the spirit is willing but the flesh is weak” as “the vodka is good but the meat is rotten” illustrates the difficulties en- countered. In 1966, a report by an advisory committee found that “there has been no machine translation of general scientific text, and none is in immediate prospect.” All U.S. government funding for academic translation projects was canceled. Today, machine translation is an im- perfect but widely used tool for technical, commercial, government, and Internet documents.
The second kind of difficulty was the intractability of many of the problems that AI was attempting to solve. Most of the early AI programs solved problems by trying out different combinations of steps until the solution was found. This strategy worked initially because microworlds contained very few objects and hence very few possible actions and very short solution sequences. Before the theory of computational complexity was developed, it was widely thought that “scaling up” to larger problems was simply a matter of faster hardware and larger memories. The optimism that accompanied the development of resolution theorem proving, for example, was soon dampened when researchers failed to prove theorems involv- ing more than a few dozen facts.The fact that a program can find a solution in principle does not mean that the program contains any of the mechanisms needed to find it in practice.
The illusion of unlimited computational power was not confined to problem-solving programs. Early experiments inmachine evolution(now calledgenetic algorithms) (Fried-
MACHINE EVOLUTION GENETIC
ALGORITHM berg, 1958; Friedberg et al., 1959) were based on the undoubtedly correct belief that by making an appropriate series of small mutations to a machine-code program, one can gen- erate a program with good performance for any particular task. The idea, then, was to try random mutations with a selection process to preserve mutations that seemed useful. De- spite thousands of hours of CPU time, almost no progress was demonstrated. Modern genetic algorithms use better representations and have shown more success.
22 Chapter 1. Introduction Failure to come to grips with the “combinatorial explosion” was one of the main criti- cisms of AI contained in the Lighthill report (Lighthill, 1973), which formed the basis for the decision by the British government to end support for AI research in all but two universities.
(Oral tradition paints a somewhat different and more colorful picture, with political ambitions and personal animosities whose description is beside the point.)
A third difficulty arose because of some fundamental limitations on the basic structures being used to generate intelligent behavior. For example, Minsky and Papert’s bookPercep- trons(1969) proved that, although perceptrons (a simple form of neural network) could be shown to learn anything they were capable of representing, they could represent very little. In particular, a two-input perceptron (restricted to be simpler than the form Rosenblatt originally studied) could not be trained to recognize when its two inputs were different. Although their results did not apply to more complex, multilayer networks, research funding for neural-net research soon dwindled to almost nothing. Ironically, the new back-propagation learning al- gorithms for multilayer networks that were to cause an enormous resurgence in neural-net research in the late 1980s were actually discovered first in 1969 (Bryson and Ho, 1969).
1.3.5 Knowledge-based systems: The key to power? (1969–1979)
The picture of problem solving that had arisen during the first decade of AI research was of a general-purpose search mechanism trying to string together elementary reasoning steps to find complete solutions. Such approaches have been calledweak methodsbecause, although
WEAK METHOD
general, they do not scale up to large or difficult problem instances. The alternative to weak methods is to use more powerful, domain-specific knowledge that allows larger reasoning steps and can more easily handle typically occurring cases in narrow areas of expertise. One might say that to solve a hard problem, you have to almost know the answer already.
The DENDRALprogram (Buchananet al., 1969) was an early example of this approach.
It was developed at Stanford, where Ed Feigenbaum (a former student of Herbert Simon), Bruce Buchanan (a philosopher turned computer scientist), and Joshua Lederberg (a Nobel laureate geneticist) teamed up to solve the problem of inferring molecular structure from the information provided by a mass spectrometer. The input to the program consists of the ele- mentary formula of the molecule (e.g., C6H13NO2) and the mass spectrum giving the masses of the various fragments of the molecule generated when it is bombarded by an electron beam.
For example, the mass spectrum might contain a peak atm= 15, corresponding to the mass of a methyl (CH3) fragment.
The naive version of the program generated all possible structures consistent with the formula, and then predicted what mass spectrum would be observed for each, comparing this with the actual spectrum. As one might expect, this is intractable for even moderate-sized molecules. The DENDRAL researchers consulted analytical chemists and found that they worked by looking for well-known patterns of peaks in the spectrum that suggested common substructures in the molecule. For example, the following rule is used to recognize a ketone (C=O) subgroup (which weighs 28):
ifthere are two peaks atx1andx2such that
(a)x1+x2=M + 28(M is the mass of the whole molecule);